AI agents for finance: Main components and implementation 

The across-the-board advent of cutting-edge know-how like blockchain technology and artificial intelligence (machine learning, generative AI, computer vision, natural language processing, and others) has dramatically altered workflows in multiple domains of the early third millennium. One of the emerging AI-driven products disrupting the tech-savvy business world today is AI agents. They are smart solutions capable of perceiving the environment, analyzing it, making conclusions, and accomplishing an assignment set before them, continuously improving their performance over time. Moreover, for solving complex problems, multi-agent collaboration is possible.  

Being extremely useful for doing routine tasks modern businesses face in their shop floor activities, AI agents are widely leveraged in numerous verticals. What about the finance and banking industry? Let’s find out the role of AI agents in this field, their niche-specific architecture, advantages, and use cases.  

AI agents in finance

The increase in the consumer audience, growth of the range of financial services, and rapidly changing market conditions call for new tools and approaches that can streamline operations and automate the workflows of companies working in this sector. Such novelties are often associated with using AI that gathers momentum across the contemporary business environment.  

Financial institutions stand to benefit from leveraging this technology immensely. Among the organizations harnessing AI agents, 91% admit their enormous capabilities in fraud detection and risk assessment, 82% report significant OPEX reduction, and 43% emphasize business efficiency improvement. They are also appreciated by consumers, 37% of whom have enjoyed the assistance from AI-powered chatbots in receiving financial services.  

It is natural to assume that such impressive statistics should have led to sizable investments in financial AI agent development and implementation. Nothing of the kind. Despite the fact that leveraging AI agents in financial operations is predicted to generate $280 billion in sales revenues by 2032, only 32% of organizations in this realm employ them for solving financial tasks. Why, you may ask? Probably, financial companies and banks do not realize how AI agents differ from large language models (LLMs) they utilize in their pipeline processes.  

Financial AI agents vs. LLMs

While conventional LLMs can handle versatile operations in diverse business settings, financial AI agents are honed to tackle more specific tasks in the sector. This niche focus gives the latter a sharp edge over the former. Let’s look closer at key aspects of both technologies’ functioning. 

Being trained on huge datasets from a wide scope of domains, LLMs possess a broad knowledge base they rely on in content generation, data processing, and answering basic questions. Financial AI agents are geared for a narrow field of expertise, so they excel at providing actionable recommendations and advice concerning financial markets dynamics, investment strategies, portfolio management, credit risk assessment, and the like.  

Both technologies enhance decision-making. However, LLMs take into account various factors and can be applied across multiple topics, whereas financial AI agents’ algorithms are designed for making informed decisions in the sphere of finance only. The same is true of compliance. While LLMs have general compliance characteristics, making them suitable for a broad gamut of verticals, financial AI agents are crafted to align with stringent regulations symptomatic of the industry.  

Such characteristics of financial AI agents are enabled via their unique structure.  

Architecture of AI agents in finance

The inner organization of a financial AI agent is based on a multi-tiered principle specifically honed to solve complex tasks in the realm. There are five basic layers that work in sync to provide robust performance and enhance efficiency of the entire system’s functioning. Let’s see how it works by the example of market forecasting. 

Financial AI agents layer 

The key component of this tier is Chain-of-Thought (CoT) prompting that breaks financial problems into sequential steps (in this case, related to market dynamics), following which the tool can address them.  

First, the user activates the agent by initiating the query, and it utilizes NLP techniques to understand the user’s intent. Then, the agent employs various APIs to connect to multiple data sources like market news and financial records of different kinds and selects relevant visual and textual information. Besides, the initial data processing is performed here to prepare it for deeper analysis.  

Financial LLMs algorithms layer 

Now, specifically geared multimodal models boosted by machine learning mechanisms come into play. They conduct regional market and document analyses, which result in generating detailed instructions concerning future actions.  

LLMOps and DataOps layers 

These operate in close alignment to manage the choice and integration of various LLMs regarding their performance. The first layer considers the most effective outcomes of the given financial scenarios and adjusts model deployment correspondingly. The second one exercises real-time financial data processing, keeping the model responsive to fluctuating market trends and other changes in the area.  

Multi-source LLM foundation models layer 

This layer’s responsibility lies in ensuring all models are up-to-date, optimized, compatible with plug-and-play functionalities of other LLMs, and consistent with the latest data standards and financial technologies. Thanks to this tier, LLMs play well with each other, providing the system’s seamless operation.  

Market forecasting insights obtained as deliverables of the layers’ integrated functioning are then utilized for strategic management of resources, portfolio adjustments, and report generation. During the whole procedure, the system applies self-assessment and adaptive learning mechanisms to support feedback loops leveraged in evaluating decision quality, eliminating errors, optimizing algorithms, and increasing performance accuracy.  

Thanks to such sophisticated architecture, AI agents are employed by financial institutions in many industrial workflows. 

AI agents’ use cases in finance

DICEUS has delivered numerous fintech products, so we know well where financial AI agents bring maximum value. 

Personalized portfolio management 

Financial AI agents exercise real-time market analysis and simultaneously evaluate the current portfolio’s performance. Such parallel processing allows them to pinpoint missed opportunities and generate customized investment plans, offering solutions for capitalizing on market volatilities, automating trades, and aligning suggested steps with the customer’s individual financial goals and risk tolerance.  

Automation of compliance and risk management 

To perform high-quality risk assessment, AI agents automatically distribute transaction data, activities, and financial contracts across pre-defined risk categories. The accuracy and efficiency of this AI-driven procedure allow compliance specialists to avoid related time- and effort-consuming manual processes and focus instead on risk mitigation as a part of compliance management.  

Fraud identification and prevention 

Banking scams and fraudulent transactions are two main threats for the industry that inflicted $485.6 billion losses on financial institutions in 2023 alone. Existing systems of fraud detection relying on manual techniques prove inefficient for handling vast quantities of financial data, which leaves you with no alternative but to leverage AI agents for it. They sift through tons of records, emails, messages, websites, etc., identify patterns symptomatic of fraud, and institute preventive measures to nip the problem in the bud. 

Customer service automation 

Today, efficient and fast customer service is impossible without conversational AI agents that power chatbots and virtual assistants employed by financial organizations. Thanks to them, the company can streamline and facilitate request and inquiry handling, ticket generation and resolution, customer interaction management, consumer satisfaction assessment, and more, performing top-notch reporting and analytics while doing all these tasks. 

Taxation management 

All operations in this field are executed faster and more accurately thanks to AI agents. They help automate tax information categorization and validation for compliance, detect inconsistencies in transaction data for auditing, prepare and file tax forms, analyze current data to suggest tax-efficient strategies, optimize tax positions, and more. 

Support for sales operations 

Surveys claim that up to 85% of intraday stock traders find themselves in the red because they don’t consider the market situation, lack trading discipline, and ignore capital limits. Financial AI agents ensure profitable sales across various aspects by introducing algorithm-based trading. It allows employees to adopt quick and consistent decisions, eliminating emotional factors from the process. 

Credit underwriting 

Making credit and lending decisions is a slow and laborious procedure that, on average, takes from 3 to 4 weeks if performed manually. Specialists have to verify the applicant’s income and employment across multiple data sources, evaluate their assets, assess collateral, adjust credit scores, and only then adopt the final lending (or not lending) decision. With AI agents, this time can be radically reduced, whereas the creditworthiness assessment becomes more accurate and error-free. 

When leveraged by skilled professionals in the sector, AI agents in finance can usher in weighty boons for both organizations and their customers. 

Benefits of financial AI agents

Banks and financial institutions that have harnessed AI agents in their shop floor routines report the following perks of their usage.  

Round-the-clock availability 

AI agents know nothing of working hours, days off, vacations, or sick leaves. They function ceaselessly, providing 24/7 service, which is mission-critical for an uninterrupted continuity of financial operations. Besides, it guarantees the best-in-class customer experience, thus improving people’s satisfaction from interacting with the company and augmenting brand loyalty.  

Enhanced data processing 

Modern financial companies handle huge quantities of business, financial, and client data. Processing them manually would take ages, and the quality of such effort is substandard. AI agents can do it swiftly and accurately, pinpointing patterns and identifying anomalies, which is vital for investment planning, risk assessment, fraud detection, and other future-proofing activities. 

Large-scale automation 

All repetitive and routine tasks (for instance, document processing, invoice generation, data retrieval, ticket resolution, etc.) can be delegated to AI agents that will perform them quickly and error-free. Their automation relieves the pressure on human personnel, allowing them to focus on more complex, strategic, or creative assignments. 

Increased operational efficiency 

When the lion’s share of back-office and client-facing activities are automated, the organization’s efficiency and productivity soar. As a result, the staff can do more within the working day, which hones the company’s competitive edge. 

Reduced OPEX 

All-embracing automation of finance workflows reduces the need for human workforce involvement. That is why financial organizations leveraging AI agents save a pretty penny that would otherwise be spent on personnel hiring, training, upskilling, and salaries. Moreover, AI agents monitor resource allocation and reveal hidden expenditures, which also minimizes monetary losses and optimizes resource and capital utilization. 

Streamlined compliance 

The financial realm is one of the most heavily regulated. It is a job of work for the staff to keep track of all current standards and watch for the appearance of new ones. AI agents can be configured to monitor the legal framework sector and identify the company’s operations that fail to comply with regulations. Besides, compliance audits are essentially facilitated via AI agent employment. 

Error minimization 

Machines don’t make mistakes, which in finance may cost organizations an arm and a leg. That is why leveraging AI agents for financial workflows increases their accuracy, rules out the possibility of errors, and helps diminish false positives, which is pivotal in credit underwriting and fraud detection. 

Robust predictive analytics 

The insights AI agents derive from numerous financial data points enable analytical teams to envisage possible developments and forecast trends. Such AI agents’ capabilities are highly instrumental in risk assessment, strategic planning, investment management, and other financial workflows related to upcoming events.  

Quality lead generation 

In the field of finance, to obtain one lead, you have to spend $272.  

Since companies are interested in attracting hundreds, if not thousands, of leads, the overall expenditure on enlarging their customer base may amount to outrageous numbers. AI agents can cut down on them by figuring out what potential customers are likely to engage with the brand and convert and directing all efforts upon them in targeted advertising and promotion campaigns.  

Service personalization 

A customer’s purchasing behavior and individual preferences serve as a source of valuable information about the person that allows for highly tailored financial services. AI agents make sure financial advice and product recommendations align with unique needs and address specific pain points of every client. Besides, a comprehensive picture of the customer AI agents draw paves the way for efficient cross-selling and upselling offers the person is likely to accept.  

Improved customer satisfaction 

Quick service, informed responses to queries, absence of errors, data-driven investment recommendations, and targeted interaction with the clientele are the staples of a customer-centric approach that come together to result in a sharp increase in customer satisfaction. This, in turn, allows the financial institution to retain old clients and attract new ones, encouraging them to become brand advocates.  

To enjoy all these boons to the maximum, you should delegate financial AI agent building to a reliable IT vendor with in-depth expertise in fintech development and top-level competence in artificial intelligence. DICEUS is your number one candidate for such a role. Our vetted AI engineers can create new agents from scratch or upgrade your existing AI-fueled software to let it handle unique tasks your company needs to solve. Contact us to revolutionize your financial workflows and drive business growth. 

Summing it up

AI agents are cutting-edge innovative tools that can analyze the environment, make conclusions, adopt data-driven decisions, and improve their operation without human intervention. When developed with an eye to employment in the sector of finance and banking, they enhance data processing, automate repetitive tasks, boost operational efficiency, reduce expenditures, minimize the number of errors, streamline compliance, help generate high-quality leads, allow for exclusive service personalization, and increase customer satisfaction.  

Being specifically honed to cope with financial assignments, such AI agents align better with niche-specific goals than regular LLMs. Their five-layer architecture makes them perfect tools for personalized portfolio management, risk assessment, fraud prevention, customer service upgrading, improving credit underwriting, and more.  

Financial AI agents operate well only if created by seasoned IT professionals qualified both in AI technologies and fintech software development.  

Frequently asked questions